Diving deeper into VOCs / Jessica Lum in COATINGS TECH, Vol. 19, N° 4 (04/2022)  

[article]  inCOATINGS TECH > Vol. 19, N° 4 (04/2022) . - p. 38-46 Titre : Diving deeper into VOCs : Predicting formulation component GC-MS response factor using quantitative structure-activity relationships coupled with artificial neural networks Type de document : texte imprimé Auteurs : Jessica Lum, Auteur ; Madeline Schultz, Auteur ; Erik Sapper, Auteur Année de publication : 2022 Article en page(s) : p. 38-46 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Chromatographie en phase gazeuse Réseaux neuronaux (informatique) Revêtements -- Analyse Revêtements -- Teneur en composés organiques volatils Revêtements organiques Spectrométrie de masse Structure moléculaire Index. décimale : 667.9 Revêtements et enduits Résumé : The identification, measurement, and reduction of volatile organic compounds (VOCs) has been a key motivator in recent coatings research and development efforts. Analytical methods for determining VOC levels in organic coatings continue to improve, as chromatographic and spectroscopic approaches afford a means of quantifying VOC content directly in waterborne as well as solventborne coatings. Heuristic methods for estimating the volatility of formulation components are common but are not extensively validated using quantitative structure-property relationships. Thus, a clearer link between component transport through an evolving coating matrix during curing processes, the bulk volatility of a compound, and the elution and quantification of compounds in a gas chromatograph (GC) still must be made to promote innovation in this area. To address these issues, digital tools such as molecular descriptors and machine learning models are being combined with experimental measurements to better understand the time-dependent mechanistic nature of VOCs in coatings and to enable predictive control over the volatility and in-coating behavior of newly developed formulation components. Here, we present the development and validation of a molecular structure-based neural network for the prediction of response factor for formulation components in a gas chromatography (GC) analysis. This represents an important step in creating large-scale computational design tools that enable in silico formulation, optimization, and enduse property prediction of environmentally benign coatings. Note de contenu : - MATERIALS AND METHODS : Response factor determination by GC - Quantitative structure activity relationships for identifying molecular features relevant to response factor - RESULTS - DISCUSSION - FUTURE WORK - Table 1 : Calculated molecular descriptor with the largest positive or negative correlation with compound response factor - Table 2 : Response factors and retention times for 80 compound dataset for VOC analysis, with absolute and normalized values provided - Table 3 : Predicted and experimental response factors (absolute and normalized) in the training set - Table 4 : Predicted and experimental response factors (absolute and normalized) in the validation set En ligne : https://drive.google.com/file/d/1pBE163urWUV7WBjU9qEk0WoeUe4A422L/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37610 [article]

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Intelligent testing / Erica Bilodeau in EUROPEAN COATINGS JOURNAL (ECJ), N° 11 (11/2016)  

[article]  inEUROPEAN COATINGS JOURNAL (ECJ) > N° 11 (11/2016) . - p. 40-47 Titre : Intelligent testing : Self-tuning tests to examine coating failure pave the way for an automated future Type de document : texte imprimé Auteurs : Erica Bilodeau, Auteur ; Erik Sapper, Auteur ; Kelby Hull, Auteur ; Chad Immoos, Auteur ; Raymond Fernando, Auteur Année de publication : 2016 Article en page(s) : p. 40-47 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Algorithmes Analyse des défaillances (fiabilité) Automatisation Essais (technologie) Revêtements -- Analyse Revêtements -- Défauts Systèmes d'aide à la décision Index. décimale : 667.9 Revêtements et enduits Résumé : Developing experimental methods that continuously adapt and respond to results offers a way of accelerating coating and material design. Applying autonomuos robotics, machine learning, and decision analytics to coatings chemistry makes it possible to automate the testing of new materials. A new way of approaching coating failure enables multiple coating systems and operational environments to be compared. Note de contenu : - AC-DC-AC testing to obtain decison-making algorithm - Inevitable coating failure offers better comparison - Results are continuously applied to improve testing - One failure curve to fit all coating systems - Fine tuning test parameters helps to develop specialised test - Potential to automate algorithm to produce test parameters for specific coating systems En ligne : https://drive.google.com/file/d/1hHhXsGF5kKS7OVeY4FMPuC4teOzWHPtN/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=27136 [article]

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Robots reading recipes / Madeleine Hummel in EUROPEAN COATINGS JOURNAL (ECJ), N° 2 (02/2019)  

[article]  inEUROPEAN COATINGS JOURNAL (ECJ) > N° 2 (02/2019) . - p. 84-88 Titre : Robots reading recipes : A semantic framework for coatings science Type de document : texte imprimé Auteurs : Madeleine Hummel, Auteur ; Dom Porcincula, Auteur ; Erik Sapper, Auteur Année de publication : 2019 Article en page(s) : p. 84-88 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Anglais (langue) -- Anglais technique Formulation (Génie chimique) Polyuréthanes Revêtements bi-composant:Peinture bi-composant Index. décimale : 667.9 Revêtements et enduits Résumé : Natural language processing tools can be used to extract structured, semantic information from English text. This article shows how they can be applied to coatings formulation. Note de contenu : - Natural language processing and its role in coatings formulation - The philosophy of natural language processing - A semantic and structured framework for communicating formulation information - Fig. 1 : Tabular and prose format representations of a 2K polyurethane formulation. Product names are fictional representations and are not meant to convey any commercial endorsement - Fig. 2 : Prose format representations of a 2K polyurethane formulation that have been parsed and tagged by a natural language processing engine - Fig. 3 : Pseudocode for the formulation directive producted from semantic and syntactical natural language processing of the unstructured data for the 2K polyurethane formulation - Fig. 4 : The role of natural langage processing in an autonomous and automated formulation workflow En ligne : https://drive.google.com/file/d/1vfr1M3yHTxgzZkKBRqp9m6fX_4kq_HKa/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=31679 [article]

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